1 R Session info

print(sessionInfo(), locale = FALSE)
## R version 4.1.1 (2021-08-10)
## Platform: x86_64-pc-linux-gnu (64-bit)
## Running under: Pop!_OS 21.10
## 
## Matrix products: default
## BLAS:   /usr/lib/x86_64-linux-gnu/blas/libblas.so.3.9.0
## LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.9.0
## 
## attached base packages:
## [1] stats     graphics  grDevices utils     datasets  methods   base     
## 
## other attached packages:
##  [1] broom_0.7.7     patchwork_1.1.1 forcats_0.5.1   stringr_1.4.0  
##  [5] dplyr_1.0.6     purrr_0.3.4     readr_1.4.0     tidyr_1.1.3    
##  [9] tibble_3.1.6    ggplot2_3.3.5   tidyverse_1.3.1 here_1.0.1     
## 
## loaded via a namespace (and not attached):
##  [1] tidyselect_1.1.1 xfun_0.27        bslib_0.3.0      haven_2.4.1     
##  [5] colorspace_2.0-2 vctrs_0.3.8      generics_0.1.0   htmltools_0.5.2 
##  [9] yaml_2.2.1       utf8_1.2.2       rlang_1.0.1      jquerylib_0.1.4 
## [13] pillar_1.7.0     withr_2.4.3      glue_1.6.1       DBI_1.1.1       
## [17] dbplyr_2.1.1     readxl_1.3.1     modelr_0.1.8     lifecycle_1.0.1 
## [21] cellranger_1.1.0 munsell_0.5.0    gtable_0.3.0     rvest_1.0.0     
## [25] evaluate_0.14    knitr_1.36       fastmap_1.1.0    fansi_1.0.2     
## [29] Rcpp_1.0.8       backports_1.2.1  scales_1.1.1     jsonlite_1.7.2  
## [33] fs_1.5.0         hms_1.1.1        digest_0.6.29    stringi_1.7.6   
## [37] bookdown_0.24    rprojroot_2.0.2  grid_4.1.1       cli_3.1.1       
## [41] tools_4.1.1      magrittr_2.0.2   sass_0.4.0       crayon_1.4.2    
## [45] pkgconfig_2.0.3  ellipsis_0.3.2   xml2_1.3.2       reprex_2.0.0    
## [49] lubridate_1.7.10 assertthat_0.2.1 rmarkdown_2.11   httr_1.4.2      
## [53] rstudioapi_0.13  R6_2.5.1         compiler_4.1.1

2 Experimental setup

knitr::include_graphics(here("analysis/figures/plate_lid_side.jpg"))
The 24 deepwell plate and the lid with pegs (substrata)

Figure S2.1 The 24 deepwell plate and the lid with pegs (substrata) 

knitr::include_graphics(here("analysis/figures/plate_lid_on.jpg"))
The 24 deepwell plate with the lid (almost) on.

Figure S2.2 The 24 deepwell plate with the lid (almost) on.

4 Metadata for raw data files

Counts represent the absolute number of starches counted on a slide

starch_counts.csv

variable description
sample Sample number.
plate Plate number that the sample came from.
row Which row on the plate the sample came from.
s Small starch count.
m Medium starch count.
l Large starch count.
total Sum of s, m, and l.
treatment Treatment solution to which the samples were exposed.
starch Type of starch that was counted.
weight Weight of the biofilm sample.
vol Total volume of EDTA in which the sample was dissolved.
portion_slide Proportion of the microscope slide that was counted. Total transects on slide divided by counted transects.

solution_counts.csv

variable description
solution Type of starch in solution.
concentration Concentration (%w/v) of starch in solution.
vol_slide Volume of solution added to slide.
vol_total Total volume of solution in aliquot.
portion_slide Proportion of slide that was counted. Total transects on slide divided by counted transects.
slide Slide number.
starch Starch type counted.
s Small starch count.
m Medium starch count.
l Large starch count.
total Sum of s, m, and l.

5 Raw data

The raw data can be downloaded from GitHub:

# solution counts
wget https://github.com/bbartholdy/byoc-starch/blob/main/analysis/data/raw_data/solution_counts.csv

# sample counts
wget https://github.com/bbartholdy/byoc-starch/blob/main/analysis/data/raw_data/starch_counts.csv

Raw counts from the treatment solutions before extrapolation.

Table: (#tab:raw-data-sol) testing

Raw counts from the calculus samples before extrapolation:

Table S5.2 Raw counts from the calculus obtained by counting the number of starches on the slide.
sample plate row s m l total treatment starch weight vol portion_slide
st1A1 1 A 39 535 119 693 potato potato 5.80 100 1.0000000
st1A2 1 A 6 28 5 39 potato potato 5.81 100 1.0000000
st1A3 1 A 26 1392 389 1807 potato potato 8.22 100 1.0000000
st1A4 1 A 14 184 40 238 potato potato 4.65 100 1.0000000
st1A5 1 A 20 341 98 459 potato potato 7.68 200 1.0000000
st1A6 1 A 32 466 159 657 potato potato 7.79 200 1.0000000
st1B1 1 B 62 36 4 102 wheat wheat 5.15 100 0.1034483
st1B2 1 B 508 321 18 847 wheat wheat 4.56 100 0.2500000
st1B3 1 B 606 664 73 1343 wheat wheat 9.28 100 0.1034483
st1B4 1 B 61 51 14 126 wheat wheat 1.59 100 0.1034483
st1B5 1 B 276 227 64 567 wheat wheat 8.62 200 0.1034483
st1B6 1 B 175 96 19 290 wheat wheat 9.80 200 0.1034483
st1C1 1 C NA 57 19 76 mix potato 4.09 100 0.1034483
st1C1 1 C 97 94 50 241 mix wheat 4.09 100 0.1034483
st1C2 1 C NA 12 13 25 mix potato 1.50 100 0.1034483
st1C2 1 C 31 30 9 70 mix wheat 1.50 100 0.1034483
st1C3 1 C NA 113 20 133 mix potato 8.44 100 0.1034483
st1C3 1 C 351 256 39 646 mix wheat 8.44 100 0.1034483
st1C4 1 C NA 78 25 103 mix potato 5.42 100 0.1034483
st1C4 1 C 392 302 68 762 mix wheat 5.42 100 0.1034483
st1C5 1 C NA 22 10 32 mix potato 6.12 200 1.0000000
st1C5 1 C 5 0 0 5 mix wheat 6.12 200 1.0000000
st1C6 1 C NA 17 0 17 mix potato 1.91 100 1.0000000
st1C6 1 C 97 52 12 161 mix wheat 1.91 100 1.0000000
st1D1 1 D NA NA NA 1 control none 6.51 100 1.0000000
st1D2 1 D NA NA NA 0 control none 4.42 100 1.0000000
st1D3 1 D NA NA NA 0 control none 5.01 200 1.0000000
st1D4 1 D NA NA NA 0 control none 5.14 100 1.0000000
st1D5 1 D NA NA NA 0 control none 4.51 100 1.0000000
st1D6 1 D NA NA NA 0 control none 1.67 NA NA
st2A1 2 A 20 150 24 194 potato potato 6.11 200 1.0000000
st2A2 2 A 89 479 34 602 potato potato 2.54 100 1.0000000
st2A3 2 A 71 370 22 463 potato potato 8.48 200 1.0000000
st2A4 2 A 59 773 135 967 potato potato 5.91 200 1.0000000
st2A5 2 A 97 512 292 901 potato potato 8.92 200 1.0000000
st2A6 2 A NA NA NA NA potato potato 3.14 NA NA
st2B1 2 B 183 130 20 333 wheat wheat 8.08 200 0.1034483
st2B2 2 B 27 19 3 49 wheat wheat 2.30 100 0.1034483
st2B3 2 B 585 409 43 660 wheat wheat 6.84 100 0.1034483
st2B4 2 B 32 21 2 55 wheat wheat 0.56 100 0.1034483
st2B5 2 B 308 263 46 617 wheat wheat 8.51 200 0.1034483
st2B6 2 B NA NA NA NA wheat wheat 1.06 NA NA
st2C1 2 C NA 79 17 96 mix potato 5.04 100 0.1034483
st2C1 2 C 521 331 58 910 mix wheat 5.04 100 0.1034483
st2C2 2 C NA 25 1 26 mix potato 3.64 100 0.1034483
st2C2 2 C 182 101 25 308 mix wheat 3.64 100 0.1034483
st2C3 2 C NA 31 4 35 mix potato 4.11 100 0.1034483
st2C3 2 C 252 142 19 413 mix wheat 4.11 100 0.1034483
st2C4 2 C NA 43 13 56 mix potato 3.61 100 0.1034480
st2C4 2 C 327 222 45 594 mix wheat 3.61 100 0.1034480
st2C5 2 C NA 14 0 14 mix potato 3.17 100 1.0000000
st2C5 2 C 14 8 0 22 mix wheat 3.17 100 1.0000000
st2C6 2 C NA NA NA NA mix potato 1.75 NA NA
st2D1 2 D 0 0 0 0 control none 8.32 100 1.0000000
st2D2 2 D 0 0 0 0 control none 11.18 200 1.0000000
st2D3 2 D NA NA NA NA control none 3.43 NA NA
st2D4 2 D NA NA NA NA control none 5.76 NA NA
st2D5 2 D NA NA NA NA control none 3.66 NA NA
st2D6 2 D NA NA NA NA control none 5.67 NA NA

6 Microscope images

Image of starch granules extracted from a potato treatment sample

Figure S6.1 Image of starch granules extracted from a potato treatment sample 

knitr::include_graphics(here("analysis/figures/SNAP-103412-0006.jpg"))

knitr::include_graphics(here("analysis/figures/SNAP-164650-0012.jpg"))
Microscope image of wheat starch from a wheat treatment sample.

Figure S6.2 Microscope image of wheat starch from a wheat treatment sample.

7 Amylase activity

Amylase activity in U/mL enzyme, where U is the amount of \(\mu\)moles maltose released from starch in six minutes at 36 °C.

Tables containing the amylase activity results for both plates and both photometric readings conducted on each plate. Samples (rows) were analysed in triplicates (columns).

# table of results reported in units amylase per mL enzyme (but let's be honest,
  # ...it doesn't really matter what the unit is. No activity is no activity)
cols <- c("1", "2", "3")  # sample triplicates
rows <- c("S1", "S2", "S3", "B1", "B2", "B3", "B4", "B5", "BT1", "BT2", "BT3")
plt1_ph1_result <- rbind(sal1_ph1, bmm1_ph1)
rownames(plt1_ph1_result) <- rows
plt1_ph2_result <- rbind(sal1_ph2, bmm1_ph2)
rownames(plt1_ph2_result) <- rows
plt2_ph1_result <- rbind(sal2_ph1, bmm2_ph1)
rownames(plt2_ph1_result) <- rows
plt2_ph2_result <- rbind(sal2_ph2, bmm2_ph2)
rownames(plt2_ph2_result) <- rows
plt1_ph1_result # plate 1, photometric reading 1
plt1_ph2_result # plate 1, photometric reading 2
plt2_ph1_result # plate 2, photometric reading 1
plt2_ph2_result # plate 2, photometric reading 2

8 Control samples

raw_counts %>%
  filter(treatment == "control") %>%
  select(!c(vol, portion_slide, s, m, l))

Only the total starch count was considered for control samples, as size was deemed irrelevant.

9 Count corrections

Slide transects were calculated by counting the number of transects on the cover slip under the microscope. This was done by starting in the bottom-left corner, and counting the total number of full fields-of-view across the cover slip to the bottom-right corner. The total number of transects was 29 (verified multiple times).

A 1 mL aliquot of each of the original treatment solutions was taken, from which 10 \(\mu\)L was taken and mounted on a microscope slide and mixed with 10 \(\mu\)L 20% (v/v) glycerol. Solution counts were extrapolated from a slide (10 \(\mu\)L) to the quantity in a 1 ml solution, and then multipled by 16 days to achieve the total number of granules that were exposed to the samples:

\[ \text{corrected count} = \text{raw count} \times \frac{\text{total slides}}{\text{counted slides}} \times 100 \mu L \times 16 \text{ days} \]

Samples were submerged in 50–100 \(\mu\)L EDTA, from which 20 \(\mu\)L was mounted on a microscope slide (\(V_{slide}\)) and counted. Sample counts were extrapolated to the full volume of EDTA (\(V_{sample}\)) in which the sample was submerged (i.e. 50–100 \(\mu\)L).

\[ \text{Corrected count} = \text{raw count} \times (\text{portion of slide})^{-1} \times \frac{V_{sample}}{V_{slide}} \]

10 Some additional plots

Bar plot for the total count of granules exposed to the samples over the duration of the experiment,

sol_long %>%
  filter(size == "total") %>%
  group_by(treatment, starch) %>%
  ggplot(aes(x = treatment, y = count, fill = treatment, col = starch)) +
  geom_col(size = 1.5) +
  theme(panel.background = element_rect(fill = "white"),
        panel.grid = element_line(colour = "grey"),
        panel.grid.major.x = element_blank(),
        axis.title.x = element_blank()) +
  scale_fill_viridis_d() +
  scale_color_viridis_d(begin = 0.5)

and box plot with superimposed points (with added jitter) for the extrapolated mean counts of granules extracted from the samples.

corr_comb %>%
  filter(treatment != "control") %>%
  ggplot(aes(x = treatment, y = total, 
             shape = treatment)) +
    geom_boxplot(aes(fill = treatment), alpha = 0.5) +
    geom_jitter(aes(col = treatment), width = 0.3, size = 2) +
    scale_color_viridis_d() +
    theme(panel.background = element_rect(fill = "white"),
        panel.grid = element_line(colour = "grey"),
        panel.grid.major.x = element_blank(),
        axis.title.x = element_blank()) + # remove y-axis title
    scale_fill_viridis_d()

Extracted-granule counts separated by treatment and size, including error bars:

corr_counts_long %>%
  filter(size != "total",
         treatment != "control") %>%
  group_by(treatment, starch, size) %>%
  summarise(sd = sd(count, na.rm = T),
            count = mean(count, na.rm = T)) %>%
  #mutate(percent = count / sum(count, na.rm = T) * 100) %>% 
  ggplot(aes(x = starch, y = count, fill = size)) +
    geom_col(position = "dodge") +
    geom_errorbar(aes(ymin = count, ymax = count + sd), width = 0.2, position = position_dodge(0.9)) +
    facet_wrap(~ treatment, scales = "free") +
    scale_fill_viridis_d() +
    theme_bw()
## `summarise()` has grouped output by 'treatment', 'starch'. You can override
## using the `.groups` argument.
## Warning: Removed 1 rows containing missing values (geom_col).
l = large, m = medium, s = small.

Figure S10.1 l = large, m = medium, s = small.

Size distribution (in %) within the solutions (top) and samples (bottom):

sol_size_pl <- sol_corr %>%
  group_by(solution, starch) %>%
  summarise(across(c(s, m, l, total), mean, na.rm = T)) %>%
  pivot_longer(cols = c(s,m,l, total), values_to = "count", names_to = "size") %>%
  filter(size != "total") %>%
  group_by(solution, starch) %>%
  mutate(percent = count / sum(count, na.rm = T) * 100) %>% 
  ggplot(aes(x = starch, y = percent, fill = size)) +
    geom_col(position = "dodge") +
    facet_wrap(~ solution, scales = "free_x") +
    scale_fill_viridis_d() +
    theme_bw() +
    labs(x = "")

samp_size_pl <- corr_counts_long %>%
  filter(size != "total",
         treatment != "control") %>%
  group_by(treatment, starch, size) %>%
  summarise(count = mean(count, na.rm = T)) %>%
  mutate(percent = count / sum(count, na.rm = T) * 100) %>% 
  ggplot(aes(x = starch, y = percent, fill = size)) +
    geom_col(position = "dodge") +
    facet_wrap(~ treatment, scales = "free_x") +
    scale_fill_viridis_d() +
    theme_bw()

sol_size_pl / samp_size_pl + plot_layout(guides = "collect")
l = large, m = medium, s = small.

Figure S10.2 l = large, m = medium, s = small.

Separated correlation plots. These are the same plots as in the main paper, just larger.

pl_cor
Scatter plot of sample weight and standardised starch count by z-score for separated treatments.

Figure S10.3 Scatter plot of sample weight and standardised starch count by z-score for separated treatments. 

pl_cor2
Scatter plot of sample weight in mg and standardised count of starch grains per mg calculus.

Figure S10.4 Scatter plot of sample weight in mg and standardised count of starch grains per mg calculus.

10.0.1 …and a table

Differences in size ratios (%) of granules between the solutions and the samples. Negative values indicate a loss of granules from solution to sample.

size_diff %>%
  mutate(across(where(is.numeric), signif, 3))